Praxis Global Alliance: Next gen business advisory using data science, technology, business research and on ground domain experience

The Digital ‘Change management’ iceberg

Every business today is going digital. Executives are busy charting the Digital Roadmaps. Data engineers are actively cleaning the data. Data scientists are cranking out the data and bots to create the smartest analytical tool. Business unit owners are excited with the engagement the consultants on the ground are generating.

It is a 18 month program. They said it will take time. They said Analytics is a journey and the analytical model (if done well) will learn itself. Yes, that’s what Artificial Intelligence is. Or, call it Machine Learning.

18 months later… the model is ready. The tool is done. Consultants have been paid (and rightfully so!). But the CEO, CIO and the Business Head are still unsure of how the particular ‘use case’ will be brought to life. In fact, they need to do bring it together quickly before all this investment and organizational excitement dies!

We, at Praxis, are increasingly seeing a lot of new clients ‘tired’ or ‘confused’ or both. Tired of working with consultants and ‘best-of-breed’ analytics companies. Confused as to why their analytics cruise ship is not going anywhere despite having committed the best resources to the initiatives.

The common thread we see is that they are all scraping through the ‘Change management’ iceberg. And there is nobody at the ‘bow’ of the ship ensuring that the ship is cruising towards the intended destination.

In particular, there are fivechange management aspects that we address head on in any analytics engagement making sure that outcomes are realized:

Orchestrating ‘To Be’ cross-functional coordination (not just the ‘As is’ coordination): continuously clarifying roles, transitioning of interfaces, handoffs of information and data, among many other things.

Fixing the root causes of data contamination, not just the symptoms: Data structure and data capturing process in most organizations is not perfect. Correcting the data once without putting a permanent process to capture data adequately will lead to Analytics outcomes not being as desired.

Integrating with existing systems and processes: Integrating data sources with the main analytical engines and then, integrating the output visualizations with existing Enterprise systems like Salesforce or SAP needs to be planned as it is necessary for adoption of most analytics use cases.

Aligning incentives right: As analytics allows the business to control process or customer experience better, or managing costs better, KPIs and metrics of respective functions and business leaders need to be upgraded. Without this, the organization might not adopt the digital and analytical tools quickly.

Training the client teams to be Agile: Most organizations continually improve themselves in their operations, sales and distribution, customer experience improvement, etc. But when it comes to IT or professional advise, they have been trained to work based on deliverables-milestones-deadlines. This often leaves a lot to be desired in most situations. That is why, we try to work with a ‘lean cost-but-longer duration’ model to make change happen and stick.